Open AccessJournal Article
Pattern Classification Method for Electronic Noses Based on Olfactory Neural Network Using Time Series
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TL;DR: The method not only makes the electronic nose work more similar to the way in which biological noses do, but also performs better than the conventional method with feature extraction when classifying 6 volatile organic compounds.
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Abstract: Extracting appropriate features from the responses of chemical sensor array is the first step when applying conventional artificial neural networks to electronic noses for pattern recognition.This paper presents a novel method directly dealing with time series of the sensors' responses based on an olfactory neural network with many dynamic properties.The method not only makes the electronic nose work more similar to the way in which biological noses do,but also performs better than the conventional method with feature extraction when classifying 6 volatile organic compounds.
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Citations
Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application
TL;DR: It is concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate.
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Progress in bionic information processing techniques for an electronic nose based on olfactory models
TL;DR: The integration of various phenomena and their mechanisms for biological olfaction into an electronic nose context for information processing will not only make them more bionic, but also perform better than conventional methods.
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